A computer-implemented method is provided for training a global machine learning model using a learning server and a set of vehicle agents connected to roadside units (RSUs). The method includes steps of selecting vehicle agents from a pool of the vehicle agents connected to the RSUs, associating the selected vehicle agents and the RSUs respectively based on distances from the selected vehicle agents to the RSUs configured to provide measurements of the distances to the learning server, and transmitting a global model, a selected agent set and deadline thresholds in each global training round to the RSUs configured to transmit the global model and training deadlines to the selected vehicle agents. The associated RSUs compute the training deadlines of the corresponding selected vehicle agents and the selected vehicle agents locally train the global model independently using the local datasets collected by the on-board sensors of the selected vehicle agents to generate locally trained models. The method further includes aggregating the locally trained models from the selected vehicle agents via the associated RSUs to update the global model until the global model reaches an expected level of precision.
Communication and Computation Aware Distributed Machine Learning for Vehicular Networks
2023-08-24
Patent
Elektronische Ressource
Englisch